Ab Test Generator

A/B Test Generator

Generate A/B test variants for affiliate content — headlines, CTAs, landing page sections, email subject lines, and social post hooks. Each variant includes a hypothesis explaining why it might outperform the original. Output is a Markdown document with the original, variants, hypotheses, and a test plan.

Stage

S6: Analytics — Small changes in headlines and CTAs can swing conversion rates by 20-50%. A/B testing is how professional affiliates systematically find what converts best. This skill removes the guesswork by generating theory-driven variants using proven copywriting frameworks.

When to Use

• User wants to improve conversion rates on existing content
• User has a headline, CTA, or email subject line and wants alternatives
• User says "test my headline", "optimize my CTA", "A/B test ideas"
• User has a landing page section that isn't converting
• User wants to compare different messaging approaches
• Chaining from S2-S5: take any content output and generate test variants

Input Schema

original: string               # REQUIRED — the content to test (headline, CTA, paragraph,
                               # email subject line, or full social post)

content_type: string           # REQUIRED — "headline" | "cta" | "landing_section"
                               # | "email_subject" | "social_hook"

goal: string                   # OPTIONAL — "clicks" | "signups" | "purchases"
                               # Default: "clicks"

num_variants: number           # OPTIONAL — number of variants to generate (2-5)
                               # Default: 3

audience: string               # OPTIONAL — who sees this content
                               # (e.g., "SaaS founders", "content creators")

product: string                # OPTIONAL — product being promoted

Chaining context: If S2-S5 content exists in conversation, the user can reference it: "test the headline from my blog post" or "generate CTA variants for my landing page."

Workflow

Step 1: Analyze Original Content

Break down the original into components:

• Emotional angle: What emotion does it trigger? (curiosity, fear, desire, urgency)
• Specificity: How specific vs vague?
• Structure: Question, statement, command, statistic?
• Framework: Which copywriting framework does it follow? (PAS, AIDA, 4U, BAB)

Step 2: Identify Testable Elements

Determine what to vary:

• Emotional angle (switch from curiosity to urgency)
• Specificity (add numbers, remove vagueness)
• Structure (question vs statement)
• Length (shorter vs longer)
• Power words (swap key words for stronger alternatives)
• Social proof (add or remove)

Step 3: Generate Variants

Create num_variants alternatives, each using a different approach:

• Variant A: Different emotional angle
• Variant B: Different structure/format
• Variant C: Different specificity level
• Additional variants explore social proof, urgency, or contrarian angles

Each variant must:

• Preserve the core message and product reference
• Preserve any FTC disclosure from the original
• Be a realistic alternative (not just a word swap)

Step 4: Write Hypotheses

For each variant, explain:

• What was changed and why
• Which copywriting principle supports the change
• What behavior change is expected (e.g., "Higher CTR because questions create open loops")

Step 5: Suggest Test Plan

Recommend:

• Sample size needed (minimum 100 impressions per variant for social, 500 for landing pages)
• Test duration (7-14 days minimum)
• What metric to track (CTR, conversion rate, revenue per visitor)
• When to declare a winner (95% statistical significance or practical significance threshold)

Step 6: Self-Validation

Before presenting output, verify:

• 3-5 distinct variants generated (not just word swaps)
• Each hypothesis grounded in a copywriting principle or framework
• Sample size calculation is present and realistic
• Test duration is ≥7 days minimum
• Winner criteria defined with statistical significance threshold

If any check fails, fix the output before delivering. Do not flag the checklist to the user — just ensure the output passes.

Output Schema

output_schema_version: "1.0.0"  # Semver — bump major on breaking changes
test:
  original: string
  content_type: string
  goal: string

variants:
  - label: string              # "Variant A", "Variant B", etc.
    content: string            # the variant text
    change: string             # what was changed
    framework: string          # copywriting principle used
    hypothesis: string         # why this might win

test_plan:
  sample_size: number          # per variant
  duration: string             # recommended test period
  metric: string               # what to measure
  winner_criteria: string      # when to pick a winner

Output Format

1. Original — the current content being tested
2. Variants — each variant with its content, change description, and hypothesis
3. Test Plan — sample size, duration, metric, winner criteria
4. Quick Win — if one variant is clearly stronger based on copywriting principles, call it out